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Context-Based Moving Object Trajectory Uncertainty Reduction and Ranking in Road Network

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Journal of Computer Science & Technology 2016年第1期31卷 167-184页

作者： jian Dai Zhi-Ming Ding jia-jie xu School of Computer Science Beijing University of Technology Beijing 100124 China University of Chinese Academy of Sciences Beijing 100049 China Institute of Software Chinese Academy of Sciences Beijing 100190 China School of Computer Science and Technology Soochow University Suzhou 215000 China

To support a large amount of GPS data generated from various moving objects, the back-end servers usually store low-sampling-rate trajectories. Therefore, no precise position information can be obtained directly from the back-end servers and uncertainty is an inherent characteristic of the spatio-temporal data. How to deal with the uncertainty thus becomes a basic and challenging problem. A lot of researches have been rigidly conducted on the uncertainty of a moving object itself and isolated from the context where it is derived. However, we discover that the uncertainty of moving objects can be efficiently reduced and effectively ranked using the context-aware information. In this paper, we focus on context- aware information and propose an integrated framework, Context-Based Uncertainty Reduction and Ranking （CURR）, to reduce and rank the uncertainty of trajectories. Specifically, given two consecutive samplings, we aim to infer and rank the possible trajectories in accordance with the information extracted from context. Since some context-aware information can be used to reduce the uncertainty while some context-aware information can be used to rank the uncertainty, to leverage them accordingly, CURR naturally consists of two stages： reduction stage and ranking stage which complement each other. We also implement a prototype system to validate the effectiveness of our solution. Extensive experiments are conducted and the evaluation results demonstrate the efficiency and high accuracy of CURR.

关键词： moving object trajectory uncertainty reduction road network context-aware information

Enriching Context Information for Entity Linking with Web Data

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Journal of Computer Science & Technology 2020年第4期35卷 724-738页

作者： Yi-Ting Wang jie Shen Zhi-xu Li Qiang Yang An Liu Peng-Peng Zhao jia-jie xu Lei Zhao xun-jie Yang Institute of Artificial Intelligence School of Computer Science and TechnologySoochou University Suzhou 215006China iFLYTEK Research Suzhou 215000China State Key Laboratory of Cognitive Intelligence iFLYTEKHefei 230000China Computer Electrical and Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology Jeddah 23955Saudi Arabia Anhui Toycloud Technologg Co. Ltd.Hefei 230000Chira

Entity linking(EL)is the task of determining the identity of textual entity mentions given a predefined knowledge base(KB).Plenty of existing efforts have been made on this task using either"local"information(contextual information of the mention in the text),or"global"information(relations among candidate entities).However,either local or global information might be insufficient especially when the given text is *** get richer local and global information for entity linking,we propose to enrich the context information for mentions by getting extra contexts from the web through web search engines(WSE).Based on the intuition above,two novel attempts are *** first one adds web-searched results into an embedding-based method to expand the mention's local information,where we try two different methods to help generate high-quality web contexts:one is to apply the attention mechanism and the other is to use the abstract extraction *** second one uses the web contexts to extend the global information,i.e.,finding and utilizing more extra relevant mentions from the web contexts with a graph-based ***,we combine the two models we propose to use both extended local and global information from the extra web *** empirical study based on six real-world datasets shows that using extra web contexts to extend the local and the global information could effectively improve the F1 score of entity linking.

关键词： entity linking web search engine (WSE) attention mechanism abstract extraction

Crowd-Guided Entity Matching with Consolidated Textual Data

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Journal of Computer Science & Technology 2017年第5期32卷 858-876页

作者： Zhi-xu Li Qiang Yang An Liu Guan-Feng Liu jia Zhu jia-jie xu Kai Zheng Min Zhang School of Computer Science and Technology Soochow University Suzhou 215006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006 China School of Computer Science and Technology Soochow University Suzhou 215006 China School of Computer South China Normal University Guangzhou 510631 China School of Computer Science and Technology Soochow University Suzhou 215006 China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing 100872 China

Entity matching (EM) identifies records referring to the same entity within or across databases. Existing methods using structured attribute values (such as digital, date or short string values) may fail when the structured information is not enough to reflect the matching relationships between records. Nowadays more and more databases may have some unstructured textual attribute containing extra consolidated textual information (CText) of the record, but seldom work has been done on using the CText for EM. Conventional string similarity metrics such as edit distance or bag-of-words are unsuitable for measuring the similarities between CText since there are hundreds or thousands of words with each piece of CText, while existing topic models either cannot work well since there are no obvious gaps between topics in CText. In this paper, we propose a novel cooccurrence-based topic model to identify various sub-topics from each piece of CText, and then measure the similarity between CText on the multiple sub-topic dimensions. To avoid ignoring some hidden important sub-topics, we let the crowd help us decide weights of different sub-topics in doing EM. Our empirical study on two real-world datasets based on Amzon Mechanical Turk Crowdsourcing Platform shows that our method outperforms the state-of-the-art EM methods and Text Understanding models.

关键词： entity matching consolidated textual data crowdsourcing

Discovering Functional Organized Keyword Recommendation Point of Interest Groups for Spatial

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Journal of Computer Science & Technology 2018年第4期33卷 697-710页

作者： Yan-Xia xu Wei Chen jia-jie xu Zhi-xu Li Guan-Feng Liu Lei Zhao School of Computer Science and Technology Soochow University Suzhou 215006 China

A point of interest （POI） is a specific point location that someone may find useful. With the development of urban modernization, a large number of functional organized POI groups （FOPGs）, such as shopping malls, electronic malls, and snacks streets, are springing up in the city. They have a great influence on people＇s lives. We aim to discover functional organized POI groups for spatial keyword recommendation because FOPGs-based recommendation is superior to POIs-based recommendation in efficiency and flexibility. To discover FOPGs, we design clustering algorithms to obtain organized POI groups （OPGs） and utilize OPGs-LDA （Latent Dirichlet Allocation） model to reveal functions of OPGs for further recommendation. To the best of our knowledge, we are the first to study functional organized POI groups which have important applications in urban planning and social marketing.

关键词： functional organized point of interest (POI) group POI clustering OPG-LDA (organized point of interest group-latent Dirichlet allocation) model spatial keyword recommendation

An Efficient Framework for Multiple Subgraph Pattern Matching Models

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Journal of Computer Science & Technology 2019年第6期34卷 1185-1202页

作者： Jiu-Ru Gao Wei Chen jia-jie xu An Liu Zhi-xu Li Hongzhi Yin Lei Zhao School of Computer Science and Technology Soochow UniversitySuzhou 215006China School of Information Technology and Electrical Engineering The University of QueenslandBrisbane 4072Australia

With the popularity of storing large data graph in cloud, the emergence of subgraph pattern matching on a remote cloud has been inspired. Typically, subgraph pattern matching is defined in terms of subgraph isomorphism, which is an NP-complete problem and sometimes too strict to find useful matches in certain applications. And how to protect the privacy of data graphs in subgraph pattern matching without undermining matching results is an important concern. Thus, we propose a novel framework to achieve the privacy-preserving subgraph pattern matching in cloud. In order to protect the structural privacy in data graphs, we firstly develop a k-automorphism model based method. Additionally, we use a cost-model based label generalization method to protect label privacy in both data graphs and pattern graphs. During the generation of the k-automorphic graph, a large number of noise edges or vertices might be introduced to the original data graph. Thus, we use the outsourced graph, which is only a subset of a k-automorphic graph, to answer the subgraph pattern matching. The efficiency of the pattern matching process can be greatly improved in this way. Extensive experiments on real-world datasets demonstrate the high efficiency of our framework.

关键词： subgraph pattern matching k-automorphism label generalization

ATLRec:An Attentional Adversarial Transfer Learning Network for Cross-Domain Recommendation

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Journal of Computer Science & Technology 2020年第4期35卷 794-808页

作者： Ying Li jia-jie xu Peng-Peng Zhao Jun-Hua Fang Wei Chen Lei Zhao School of Computer Science and Technology Soochow UniversitySuzhou 215006China

Entity linking is a new technique in recommender systems to link users'interaction behaviors in different domains,for the purpose of improving the performance of the recommendation ***-based cross-domain recom-mendation aims to alleviate the data sparse problem by utilizing the domain-sharable knowledge from auxiliary ***,existing methods fail to prevent domain-specific features to be transferred,resulting in suboptimal *** this paper,we aim to address this issue by proposing an adversarial transfer learning based model ATLRec,which effec-tively captures domain-sharable features for cross-domain *** ATLRec,we leverage adversarial learning to generate representations of user-item interactions in both the source and the target domains,such that the discrimina-tor cannot identify which domain they belong to,for the purpose of obtaining domain-sharable *** each domain learns its domain-specific features by a private feature *** recommendation of each domain considers both domain-specific and domain-sharable *** further adopt an attention mechanism to learn item latent factors of both domains by utilizing the shared users with interaction history,so that the representations of all items can be learned sufficiently in a shared space,even when few or even no items are shared by different *** this method,we can represent all items from the source and the target domains in a shared space,for the purpose of better linking items in different domains and capturing cross-domain item-item relatedness to facilitate the learning of domain-sharable *** proposed model is evaluated on various real-world datasets and demonstrated to outperform several state-of-the-art single-domain and cross-domain recommendation methods in terms of recommendation accuracy.

关键词： adversarial transfer learning attention mechanism cross-domain recommendation entity linking

A Generative Model Approach for Geo-Social Group Recommendation

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Journal of Computer Science & Technology 2018年第4期33卷 727-738页

作者： Peng-Peng Zhao Hai-Feng Zhu Yanchi Liu Zi-Ting Zhou Zhi-xu Li jia-jie xu Lei Zhao Victor S. Sheng School of Computer Science and Technology Soochow University Suzhou 215006 China Department of Management Science and Information Systems Rutgers University Piscataway NJ 08854 U.S.A. Department of Computer Science University of Central Arkansas Conway 72035 U.S.A.

With the development and prevalence of online social networks, there is an obvious tendency that people are willing to attend and share group activities with friends or acquaintances. This motivates the study on group recommendation, which aims to meet the needs of a group of users, instead of only individual users. However, how to aggregate different preferences of different group members is still a challenging problem： 1） the choice of a member in a group is influenced by various factors, e.g., personal preference, group topic, and social relationship; 2） users have different influences when in diffe- rent groups. In this paper, we propose a generative geo-social group recommendation model （GSGR） to recommend points of interest （POIs） for groups. Specifically, GSGR well models the personal preference impacted by geographical information, group topics, and social influence for recommendation. Moreover, when making recommendations, GSGR aggregates the preferences of group members with different weights to estimate the preference score of a group to a POI. Experimental results on two datasets show that GSGR is effective in group recommendation and outperforms the state-of-the-art methods.

关键词： group recommendation topic model social network

Towards Effective Author Name Disambiguation by Hybrid Attention

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Journal of Computer Science & Technology 2024年第4期39卷 929-950页

作者： Qian Zhou Wei Chen Peng-Peng Zhao An Liu jia-jie xu jian-Feng Qu Lei Zhao School of Computer Science and Technology Soochow UniversitySuzhou 215006China

Author name disambiguation(AND)is a central task in academic search,which has received more attention recently accompanied by the increase of authors and academic *** tackle the AND problem,existing studies have proposed various approaches based on different types of information,such as raw document features(e.g.,co-authors,titles,and keywords),the fusion feature(e.g.,a hybrid publication embedding based on multiple raw document features),the local structural information(e.g.,a publication's neighborhood information on a graph),and the global structural information(e.g.,interactive information between a node and others on a graph).However,there has been no work taking all the above-mentioned information into account and taking full advantage of the contributions of each raw document feature for the AND problem so *** fill the gap,we propose a novel framework named EAND(Towards Effective Author Name Disambiguation by Hybrid Attention).Specifically,we design a novel feature extraction model,which consists of three hybrid attention mechanism layers,to extract key information from the global structural information and the local structural information that are generated from six similarity graphs constructed based on different similarity coefficients,raw document features,and the fusion *** hybrid attention mechanism layer contains three key modules:a local structural perception,a global structural perception,and a feature ***,the mean absolute error function in the joint loss function is used to introduce the structural information loss of the vector *** results on two real-world datasets demonstrate that EAND achieves superior performance,outperforming state-of-the-art methods by at least+2.74%in terms of the micro-F1 score and+3.31%in terms of the macro-F1 score.

关键词： author name disambiguation multiple-feature information hybrid attention pruning strategy structural information loss of vector space

Bionic mechanical design and 3D printing of novel porous Ti6Al4V implants for biomedical applications

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2019年第8期20卷 647-659页

作者： Wen-ming Peng Yun-feng Liu Xian-feng jiang Xing-tao Dong Janice Jun Dale A. Baur jia-jie xu Hui Pan xu xu Key Laboratory of E&M (Zhejiang University of Technology) Ministry of Education & Zhejiang Province Hangzhou 310023 China Department of Oral and Maxillofacial Surgery School of Dental Medicine Case Western Reserve University Cleveland OH 44106 USA Head and Neck Surgery Zhejiang Cancer Hospital Hangzhou 310022 China Oral and Maxillofacial Surgery Stomatology Hospital Affiliated to Zhejiang University School of Medicine Hangzhou 310006 China Department of Stomatology People's Hospital of Quzhou Quzhou 324000 China

In maxillofacial surgery, there is a significant need for the design and fabrication of porous scaffolds with customizable bionic structures and mechanical properties suitable for bone tissue engineering. In this paper, we characterize the porous Ti6Al4V implant, which is one of the most promising and attractive biomedical applications due to the similarity of its modulus to human bones. We describe the mechanical properties of this implant, which we suggest is capable of providing important biological functions for bone tissue regeneration. We characterize a novel bionic design and fabrication process for porous implants. A design concept of “reducing dimensions and designing layer by layer” was used to construct layered slice and rod-connected mesh structure (LSRCMS) implants. Porous LSRCMS implants with different parameters and porosities were fabricated by selective laser melting (SLM). Printed samples were evaluated by microstructure characterization, specific mechanical properties were analyzed by mechanical tests, and finite element analysis was used to digitally calculate the stress characteristics of the LSRCMS under loading forces. Our results show that the samples fabricated by SLM had good structure printing quality with reasonable pore sizes. The porosity, pore size, and strut thickness of manufactured samples ranged from (60.95± 0.27)% to (81.23±0.32)%,(480±28) to (685±31)μm, and (263±28) to (265±28)μm, respectively. The compression results show that the Young’s modulus and the yield strength ranged from (2.23±0.03) to (6.36±0.06) GPa and (21.36±0.42) to (122.85±3.85) MPa, respectively. We also show that the Young’s modulus and yield strength of the LSRCMS samples can be predicted by the Gibson-Ashby model. Further, we prove the structural stability of our novel design by finite element analysis. Our results illustrate that our novel SLM-fabricated porous Ti6Al4V scaffolds based on an LSRCMS are a promising material for bone implants, and are potential

关键词： Layered slice and rod-connected mesh structure (LSRCMS) Porous Ti6Al4V implant Bone defect repair Selective laser melting (SLM) Mechanical properties Finite element analysis